Depolarization lidar for water cloud remote sensing

1. Background: MS and Depolaization2. Short overview of the MC model

used in this work3. Depol-lidar for Water Cld remote

sensing: Model cases4. Example with Real data5. Summary

Lidar Multiple scattering

Scattering by cloud droplets of At uv-near IR is mainly forward

Photons can scatterMultiple times and remain within lidar Field-Of-View

Enhanced return w.r.t single scattering theory

1st order

2nd order 3rd order

total

4th order

Lidar FOV cone2

0( ) ( ) exp[ ( ) ]2

z

lidP z C z z z dz

For a polarization sensitive lidar MS also gives rise to:

• A Cross-polarized signal even for spherical targets.

• Depends on:

• Wavelength• Size Dist.(Reff profile)• Extinction profile• Filed Of View• Distance from Lidar

Multiple Scattering induced depolarization

In order to calculate MS enhanced signal and depol accurately Monte-Carlo approaches must be used.

What is a MC simulation ? (simple example with no variance reduction techniques)

Launch Photon packet

Determine path length until next interaction using PRNG

and Beer’s law

Determine scattering angle using PRNG and scatterer’s phase function

Loop until packet is absorbed, hits receiver or migrates too far from the

receiver fov

Loop in packet until desired SNR is

reached

ECSIM lidar Monte-Carlo model

• MC lidar model developed originally for EarthCARE (Earth Clouds and Aerosol Explorer Mission) satellite based simulations.

• Uses various “variance reduction” tricks to speed calculations up enormously compared to direct simple MC (but is still computationally expensive).

• Capable of simulations at large range of wavelengths and viewing geometries, including ground-based simulations.

Validation: Against other MC models and Observations

Validation (vs other models): Cases presented in Roy and Roy, Appl. Opts. (2km from a C1 cumulus cloud OD=5) Circ

lin

Carswell and Pal 1980: Field Obs. Roy et al. 2008: Lab results

ECSIM MC results

ECSIM vs other MC results

From Space: ECSIM MC vs CALIPSO Observations

Figure 1: Left: Histogram built using CALIPSO observations taken from Hu et al. 2009. Middle ECSIM calculations. Right: Overlap of the first two panels.

Not too long ago, motivated by the observations of highly depolarizing volcanic ash I was looking for a way to verify the depol. calibration of a lidar system I operate.

Motivated by Hu’s results for Calipso, I wondered if Strato-cu could be a good target

So I setup a script to run my MC code on several hundred cases using a simple water cloud model (Fixed LWC slope and Constant N)

The results were initially disappointing…..the resulting depol and backscatter relationships depended too much on the LWC slope and N !

Hmmm….. maybe I should look at this in some more detail from the other side.

Connection to water cloud remote sensing….

Some Examples:

A simple water cloud model is used:

Adiabatic Linear LWC profile and constant number density 1/3( )eff bR z z

D_LWC/dz = 0.5 gm-3 D_LWC/dz = 1.0 gm-3

Look-up-tables were made for several cloud-bases, different size-dist widths and receiver fovs.

Para Profiles normalize so that the peak is 1.0

Depol and `Shape’ largely a function of extinction profile but exploitable differences exist, especially at small particle sizes (depends somewhat of fov).

However at larger effective radii values then there is no size sensitivity.

Same extinction profile but different Reff profiles

Trial using one of the `blind-test’ LES scenes

WITH DRIZZLE !

Drizzle in lower part of cloud does not present a problem

Since effectively only information from the lowest 100 meters of the clouds is used. Departures from “good behavior” particularly near cloud top are problematic.

A case using real data

A real case:Cabauw: Leosphere ALS-450 355nm, 2.3 mrad fov

Comparison with uwave radiometer observations and sensitivity to size-dist width assumptions , fov and depol calibration uncertainties

Ran out of time…

….but preliminary findings are encouraging.

Summary• Lidar Depolarization measurements are an underutilized

source of information on water clouds.

• Fundamental Idea is not new…Sassen, Carswell, Pal, Bissonette, Roy, etc… have done a lot of work stretching back to the 80’s and likely earlier.

• But now with better Rad-transfer codes and much faster computers a re-visit is in order.

• The general problem (i.e. the inversion of backscatter+depol measurements to get lwc profile and Reff under general circumstances ) is complex and likely requires multiple fov measurements. However…

• Constraining the problem to adiabatic(-like) clouds simplifies things and enables one to construct a simple and fast inversion procedure. Still early days but the idea looks worth pursuing. There is A LOT of existing lidar observations it could be applied to.

• Results are insensitive to presence of drizzle drops !

• Lots of opportunities for synergy with radars, uwave radiometers and other instruments.

• Will require some thinking on how to integrate within an Ipt-like scheme.